Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Power Quality in Electrified Transportation Systems

"Power Quality in Electrified Transportation Systems" has covered interesting horizontal topics over diversified transportation technologies, ranging from railways to electric vehicles and ships. Although the attention is chiefly focused on typical railway issues such as harmonics, resonances and reactive power flow compensation, the integration of electric vehicles plays a significant role. The book is completed by some additional significant contributions, focusing on the interpretation of Power Quality phenomena propagation in railways using the fundamentals of electromagnetic theory and on electric ships in the light of the latest standardization efforts

Harmonic current sideband indicators (HCSBIs) for broken bar detection and diagnostics in cage induction motors

Induction motor bar breakages have been increasingly studied in the last decades because of economic interests in developing techniques that permit on-line, non-invasive, early detection of motor faults in power plants. This work is specifically focused on broken bar detection and fault severity assessment in three phase power cage motors fed by non-sinusoidal voltage sources. In this work some new fault indicators for rotor bar breakages detection in squirrel cage induction motors are proposed, mathematically developed and experimentally proved. They are based on the sidebands of phase current upper harmonics, and they are well suited especially for converter-fed induction motors. The ratios I(7-2s)f/I5f and I(5+2s)f/I7f , I(13-2s)f/I11f and I(11+2s)f/I13f are examples of such new indicators, and they are not dependent on load torque and drive inertia, as classical indicators do. Their frequency-dependence has been also examined both theoretically and experimentally, and it was found less remarkable with respect to other indicators. Moreover, their values increase linearly with the quantity of consecutive broken bars, almost for not too much advanced faults; on 4-poles motors they were found quietly like the per-unit number of broken bars (ratio on total bar number). An original formulation is presented for motor mathematical modeling, based on the Generalized Symmetrical Components Theory, for sidebands amplitude computation. A complete motor model (involving all the elementary machine electrical circuits, as stator belts and rotor mesh loops) has been used for computer simulations; the same model was then transformed by using some complex Fortescue’s matrices to obtain a steady-state linear solution, solvable for stator and rotor currents, in healthy and faulty conditions. By exploiting the model, the formal definition of a set of new broken bar indicators was finally obtained. Machine simulations carried out by running the complete numerical model confirmed the accuracy of the model, and the theoretical previsions. Experimental work was performed by using a square-wave inverter-fed motor with an appositely prepared cage, for easy testing with increasing number of broken bars and without motor dismounting. Moreover, extensive experimentation was carried out on three industrial motors with different power and poles number, with increasing load, frequency and fault gravity for methodology validation. Finally, the ideas exposed in this work led to a patent application, owned by the University of Rome “Sapienza”

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Supervisory Controller Validation For A Plug-In Parallel-Through-The-Road Hybrid Electric Vehicle By Software-In-The-Loop Testing

The goal of this research is to develop an operational supervisory controller for Wayne State University Hybrid Warriors\u27 hybrid electric vehicle architecture that can be transitioned easily to a hardware-in-the-loop testing environment for the 2011-2014 EcoCAR2 competition. It serves to demonstrate how model-based design, specifically software-in-the-loop testing, is effective for the initial steps in design, verification, and validation of a supervisory control strategy. Overall, the supervisory controller aims to meet all safety and functional requirements while reducing fuel consumption. The thesis starts by presenting a plug-in parallel-through-the-road architecture and its powertrain hardware components. Next, characteristics and capabilities of all significant powertrain components are explained along with the implementation of the vehicle plant model. Initial stages and preparations for the development of supervisory controller begin with applying the Design Failure Mode and Effects Analysis and identifying the functional vehicle requirements. Control strategies implemented within the supervisory controller are discussed in detail. Finally, results from the software-in-the-loop testing as well as safety critical fault mitigation are shown, to demonstrate the end product of a supervisory controller that has reached a high level of functionality and safety and therefore is ready for hardware-in-the-loop testing. Outlines are provided for extending the current work into next phases of hardware-in-the-loop testing, optimization using vehicle-in-the-loop results, and special applications such as cold-start

Industrial and Technological Applications of Power Electronics Systems

The Special Issue "Industrial and Technological Applications of Power Electronics Systems" focuses on: - new strategies of control for electric machines, including sensorless control and fault diagnosis; - existing and emerging industrial applications of GaN and SiC-based converters; - modern methods for electromagnetic compatibility. The book covers topics such as control systems, fault diagnosis, converters, inverters, and electromagnetic interference in power electronics systems. The Special Issue includes 19 scientific papers by industry experts and worldwide professors in the area of electrical engineering